Spatiotemporal Graph Transformer for Traffic Intelligence in Edge Computing

• URL: http://arxiv.org/abs/2608.04075v1
• Date: Tue, 04 Aug 2026 16:37:09 GMT
• Title: Spatiotemporal Graph Transformer for Traffic Intelligence in Edge Computing
• Abstract summary: We propose a graph Transformer framework that models spatial interactions and temporal dependencies for traffic edge forecasting.<n>The framework employs graph neural networks to capture spatial correlations among service regions and leverages Transformer-based self-attention to learn long-range temporal patterns from historical traffic observations.
• Score: 3.944882866656941
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Accurate traffic forecasting is essential for proactive resource management in edge computing, where service demand evolves dynamically across both space and time. In practical cellular edge systems, traffic exhibits strong spatial correlations among neighboring service regions and long-range temporal dependencies driven by user mobility and application behavior. Existing recurrent forecasting approaches can capture short-term dynamics but often struggle to model long-horizon traffic evolution under non-stationary conditions. To address this challenge, we propose a spatiotemporal graph Transformer framework that jointly models spatial interactions and temporal dependencies for traffic forecasting in edge computing. The framework employs graph neural networks to capture spatial correlations among service regions and leverages Transformer-based self-attention to learn long-range temporal patterns from historical traffic observations. By decoupling spatial representation learning from temporal reasoning, the proposed approach provides an effective mechanism for large-scale spatiotemporal traffic modeling. Extensive experiments on a real-world cellular network dataset demonstrate that the proposed graph Transformer consistently outperforms recurrent graph-based baselines, including GCN-RNN, GCN-LSTM, and GCN-GRU models, across multiple forecasting horizons. The resulting forecasts enable more effective proactive resource provisioning and reduce overload risk compared with reactive management strategies. These results highlight the potential of graph-enhanced attention mechanisms for building intelligent and adaptive edge computing systems.

Related papers

• Wireless Traffic Prediction with Large Language Model [54.07581399989292]
  TIDES is a novel framework that captures spatial-temporal correlations for wireless traffic prediction.<n> TIDES achieves efficient adaptation to domain-specific patterns without incurring excessive training overhead.<n>Our results indicate that integrating spatial awareness into LLM-based predictors is the key to unlocking scalable and intelligent network management in future 6G systems.
  arXiv  Detail & Related papers  (2025-12-19T04:47:40Z)
• SFADNet: Spatio-temporal Fused Graph based on Attention Decoupling Network for Traffic Prediction [4.868638426254428]
  This paper proposes an innovative traffic flow prediction network, SFADNet, which categorizes traffic flow into multiple traffic patterns based on spatial feature matrices.<n>For each pattern, we construct an independent adaptive-temporal fusion graph based on a cross-attention mechanism, employing residual graph convolution modules and time series modules.<n>Extensive experimental results demonstrate that SFADNet outperforms current state-of-the-art baseline across large four-scale datasets.
  arXiv  Detail & Related papers  (2025-01-07T09:09:50Z)
• Improving Traffic Flow Predictions with SGCN-LSTM: A Hybrid Model for Spatial and Temporal Dependencies [55.2480439325792]
  This paper introduces the Signal-Enhanced Graph Convolutional Network Long Short Term Memory (SGCN-LSTM) model for predicting traffic speeds across road networks. Experiments on the PEMS-BAY road network traffic dataset demonstrate the SGCN-LSTM model's effectiveness.
  arXiv  Detail & Related papers  (2024-11-01T00:37:00Z)
• Fusion Matrix Prompt Enhanced Self-Attention Spatial-Temporal Interactive Traffic Forecasting Framework [2.9490249935740573]
  We propose a Fusion Matrix Prompt Enhanced Self-Attention Spatial-Temporal Interactive Traffic Forecasting Framework (FMPESTF) FMPESTF is composed of spatial and temporal modules for down-sampling traffic data. We introduce attention mechanism in time modeling, and design hierarchical spatial-temporal interactive learning to help the model adapt to various traffic scenarios.
  arXiv  Detail & Related papers  (2024-10-12T03:47:27Z)
• Adaptive Hierarchical SpatioTemporal Network for Traffic Forecasting [70.66710698485745]
  We propose an Adaptive Hierarchical SpatioTemporal Network (AHSTN) to promote traffic forecasting. AHSTN exploits the spatial hierarchy and modeling multi-scale spatial correlations. Experiments on two real-world datasets show that AHSTN achieves better performance over several strong baselines.
  arXiv  Detail & Related papers  (2023-06-15T14:50:27Z)
• Adaptive Hybrid Spatial-Temporal Graph Neural Network for Cellular Traffic Prediction [19.88734776818291]
  We propose a novel deep learning network architecture to tackle the cellular traffic prediction problem. First, we apply adaptive hybrid graph learning to learn the compound spatial correlations among cell towers. Second, we implement a Temporal Convolution Module with multi-periodic temporal data input to capture the nonlinear temporal dependencies.
  arXiv  Detail & Related papers  (2023-02-28T06:46:50Z)
• PDFormer: Propagation Delay-Aware Dynamic Long-Range Transformer for Traffic Flow Prediction [78.05103666987655]
  spatial-temporal Graph Neural Network (GNN) models have emerged as one of the most promising methods to solve this problem. We propose a novel propagation delay-aware dynamic long-range transFormer, namely PDFormer, for accurate traffic flow prediction. Our method can not only achieve state-of-the-art performance but also exhibit competitive computational efficiency.
  arXiv  Detail & Related papers  (2023-01-19T08:42:40Z)
• Adaptive Graph Spatial-Temporal Transformer Network for Traffic Flow Forecasting [6.867331860819595]
  Traffic forecasting can be highly challenging due to complex spatial-temporal correlations and non-linear traffic patterns. Existing works mostly model such spatial-temporal dependencies by considering spatial correlations and temporal correlations separately. We propose to directly model the cross-spatial-temporal correlations on the spatial-temporal graph using local multi-head self-attentions.
  arXiv  Detail & Related papers  (2022-07-09T19:21:00Z)
• Learning dynamic and hierarchical traffic spatiotemporal features with Transformer [4.506591024152763]
  This paper proposes a novel model, Traffic Transformer, for spatial-temporal graph modeling and long-term traffic forecasting. Transformer is the most popular framework in Natural Language Processing (NLP) analyzing the attention weight matrixes can find the influential part of road networks, allowing us to learn the traffic networks better.
  arXiv  Detail & Related papers  (2021-04-12T02:29:58Z)
• Constructing Geographic and Long-term Temporal Graph for Traffic Forecasting [88.5550074808201]
  We propose Geographic and Long term Temporal Graph Convolutional Recurrent Neural Network (GLT-GCRNN) for traffic forecasting. In this work, we propose a novel framework for traffic forecasting that learns the rich interactions between roads sharing similar geographic or longterm temporal patterns.
  arXiv  Detail & Related papers  (2020-04-23T03:50:46Z)
• Spatial-Temporal Transformer Networks for Traffic Flow Forecasting [74.76852538940746]
  We propose a novel paradigm of Spatial-Temporal Transformer Networks (STTNs) to improve the accuracy of long-term traffic forecasting. Specifically, we present a new variant of graph neural networks, named spatial transformer, by dynamically modeling directed spatial dependencies. The proposed model enables fast and scalable training over a long range spatial-temporal dependencies.
  arXiv  Detail & Related papers  (2020-01-09T10:21:04Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.